code: def _normalize_float_categorical(col, clip_low, clip_high):
      missings = np.isnan(col)
      i_nonnan = sum(takewhile(operator.truth, missings))
      if i_nonnan != len(col):
          categorical_digits = (
              len(str(int(np.floor(col[i_nonnan])))) - 1
          )
          cat_mod = 10**categorical_digits
  
          col[missings] = 0.0
          col -= np.floor(col)
          col *= cat_mod
          np.round(col, out=col)
          col += -1.0
          col *= (clip_high - clip_low) / col.max()
          col += clip_low
  
          col[missings] = np.nan
      return col
  
file_path: interpret/python/interpret-core/interpret/utils/_synthetic.py
imports: import numpy
  from itertools import takewhile
  import operator
label_desc: Use image node_id 5 for calling a global function with example usage: _normalize_float_categorical(col, clip_low, clip_high) and returns: col